Top 10 GGUF Models Ranked by RAM Size (Download Guide)

The 30-Second Sizing Rule

Before the list, the math you'll use forever: a Q4_K_M GGUF file is roughly 0.56 bytes per parameter. Multiply the parameter count by 0.56 to get the weight file size, then add 1–2GB for the context window (KV cache) and runtime overhead.

File size (Q4_K_M) ≈ parameters × 0.56
RAM needed       ≈ file size + 1–2GB

# Examples
Qwen3 8B:   8 × 0.56 ≈ 4.9GB  → runs in ~6–7GB
Qwen3 32B:  32 × 0.56 ≈ 18GB  → needs ~20GB free
Llama 4 Scout (109B-A17B): 109 × 0.56 ≈ 61GB → needs ~64GB

💡 MoE caveat: a model like GPT-OSS 20B-A3B activates only ~3B parameters per token, but all 20B of weights load into memory. Memory scales with total parameters — that's why "A3B" models still need ~11GB at Q4. See the GGUF explainer for how quantization makes these files at all.

The Top 10, Ranked by RAM

#	Model	Q4_K_M file size	Minimum RAM	Best for
1	Qwen3 4B	~2.4GB	6GB	The 8GB all-rounder — chat, multilingual, agents
2	Gemma 3 4B	~2.4GB	6GB	Small and capable on modest hardware
3	Phi-4-mini	~2.3GB	6GB	Math and reasoning in a tiny package
4	Mistral 7B	~3.9GB	8GB	The dependable classic instruct model
5	DeepSeek-R1-Distill-8B	~4.5GB	8–10GB	Chain-of-thought reasoning on a laptop
6	Qwen3 8B	~4.9GB	10–12GB	The 16GB default — the best quality-per-GB
7	Qwen3 14B	~7.9GB	16GB	The 16GB quality pick
8	GPT-OSS 20B-A3B	~11GB	16GB	MoE efficiency — near-30B quality on 16GB
9	Qwen3 32B	~18GB	24GB GPU	The 24GB tier's best general model
10	Llama 4 Scout (109B-A17B)	~61GB	64GB RAM / 32GB GPU + offload	Big MoE for serious machines

Honorable mentions by tier: GLM 4.7 Flash 30B-A3B (~17GB, fits 20GB+), Qwen3-Coder 30B-A3B (~17GB, coding on 20GB+), Gemma 3 27B (~15GB, 24GB GPU), Phi-4 14B (~7.9GB, 16GB), and Qwen3 235B-A3B (~131GB) for 128GB+ workstations.

Download-Ready: Grab This File

Your hardware	Grab this file	Look for the suffix
8GB RAM (laptop, CPU)	Qwen3 4B or Gemma 3 4B	`-Q4_K_M.gguf`
12GB (older GPU or RAM)	Mistral 7B or Qwen3 8B (Q4)	`-Q4_K_M.gguf`
16GB (CPU or GPU)	Qwen3 14B, or GPT-OSS 20B-A3B	`-Q4_K_M.gguf`
20–24GB (RTX 3090/4090)	Qwen3 32B, or GLM 4.7 Flash 30B-A3B	`-Q4_K_M.gguf` (Q6_K if headroom)
32GB GPU or 64GB RAM	Llama 4 Scout, or Qwen3 32B at Q6_K	`-Q4_K_M.gguf` or `-Q6_K.gguf`
128GB+ workstation	Qwen3 235B-A3B, or DeepSeek-V3 quantized	`-Q4_K_M.gguf`

🎯 Naming tip: official GGUF repos name files like Qwen3-8B-Q4_K_M.gguf; community uploaders vary. Don't trust the name alone — check the file size on the download page against the table above. Sizes don't lie.

Where to Download

Hugging Face: search modelname gguf and open the model's GGUF repo — pick the Q4_K_M file that matches the size in this post.
Ollama: ollama run qwen3:8b handles the download, quantization, and setup automatically — how Ollama works.
Local AI Zone: local-ai-zone.github.io — curated, daily-updated GGUF directory with direct download links, sorted by benchmark scores and hardware fit.
GGUF Loader: ggufloader.github.io — drag the downloaded file in and chat; the live RAM/VRAM dashboard shows exactly what the model consumes.

🚀 First time? Start here

Our beginner's guide walks through the full setup, and Q4_K_M vs Q8_0 explains when to step up to a bigger quant. Running out of memory or seeing slow output? The troubleshooting section has the fixes.

Frequently Asked Questions (FAQ)

How do I know which GGUF file fits my RAM?

Use the Q4_K_M rule of thumb: a Q4_K_M file is roughly 0.56 bytes per parameter, plus 1–2GB for the context window and overhead. A 7B model is about 3.9GB of weights — add 1–2GB, and it runs comfortably in 8GB of RAM. Check the actual file size on the download page before committing.

What is the best GGUF model for 8GB of RAM?

Qwen3 4B or Gemma 3 4B at Q4_K_M (~2.4GB each) are the best all-rounders. Phi-4-mini (~2.3GB) is strong at math. All three leave room for a context window and other apps.

What is the best GGUF model for 16GB of RAM?

Qwen3 14B (~7.9GB at Q4_K_M) is the quality pick. GPT-OSS 20B-A3B (~11GB) and GLM 4.7 Flash 30B-A3B (~17GB, tight) are efficient MoE options that punch above their size. All fit in 16GB with room for context.

Why do MoE models need as much RAM as their total size?

A MoE model like GPT-OSS 20B-A3B activates only ~3B parameters per token, but all 20B of weights must be loaded into memory. Memory scales with total parameters; speed scales with active parameters. That's why 30B-A3B models still need ~17GB at Q4.

Where do I download GGUF files?

Hugging Face hosts official GGUF repos for most models (look for the Q4_K_M file), and Ollama pulls them with one command. Curated directories like Local AI Zone list quantized GGUFs with direct download links and daily updates.

What if my model is slow or out of memory?

Lower the context size, pick a smaller quantization (Q4_K_M instead of Q8_0), or move to a smaller model. For memory: Q4 instead of Q8, 8B instead of 14B. For speed: enable GPU offload or reduce context. Our troubleshooting guide covers the full list.